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Building Bulletproof Transaction Processing in Fintech
FintechDistributed SystemsPostgreSQLBackend
Building Bulletproof Transaction Processing in Fintech
When handling real money, there's zero tolerance for errors. Here's how we architected a financial transaction processing system that handles savings, loans, and investments reliably.
Core Requirements
Financial systems have unique constraints:
- Atomicity: Operations must complete fully or not at all
- Idempotency: Duplicate requests must produce the same result
- Audit Trail: Every transaction must be traceable
- Race Condition Prevention: Concurrent operations can't corrupt state
Architectural Patterns
Database Design
We used PostgreSQL with these patterns:
- Immutable Ledger: All transactions are append-only
- Double-Entry Accounting: Every debit has a corresponding credit
- Optimistic Locking: Version numbers prevent lost updates
- Serializable Isolation: Highest transaction isolation level for critical operations
Idempotency Keys
Every API request includes an idempotency key:
interface TransactionRequest {idempotencyKey: string;amount: number;fromAccount: string;toAccount: string;}
We store these keys with transaction results, ensuring duplicate requests return the same response without re-processing.
Scheduled Operations
BullMQ handles time-based financial operations:
- Automated loan repayments
- Interest calculations
- Recurring investment contributions
Jobs are designed to be safely retryable with at-least-once delivery semantics.
Testing Strategy
Financial code requires extensive testing:
- Unit tests for business logic
- Integration tests with real database transactions
- Property-based tests for invariants
- Load tests simulating concurrent operations
Lessons Learned
- Design for failure: Assume every operation can fail and handle it gracefully
- Audit everything: Comprehensive logging saved us multiple times during debugging
- Test concurrency: Race conditions only appear under load
- Keep it simple: Complex code is harder to verify and trust
Building fintech systems is challenging, but following these patterns helps create systems you can trust with real money.